Analyzing the content of video streams to determine whether or not any interesting activities or objects are present is a resource-intensive operation. Software applications are used to analyze video streams, attempting to recognize certain activities or objects in the streams. For example, recognition applications exist for recognizing faces, gestures, vehicles, guns, motion, and the like. Often, such applications are used to analyze surveillance video streams for security purposes.
Some rudimentary analyses of a video stream, such as motion detection and gross object finding, can typically be performed quickly and, therefore, can be performed in real-time as the video stream is being captured and recorded. Compared to rudimentary analyses, more complex analyses of a video stream either (1) take more time with the same resources and, therefore, are performed on recorded video rather than in real-time, or (2) require more resources to perform.
Computing architectures used to execute recognition applications require significant computing resources in order to perform computationally complex operations, and significant storage resources in order to organize, save and access the video streams being analyzed. A typical approach to systems for analyzing video streams for recognition purposes is to design the systems for peak loads. Consequently, the system is provisioned with enough computational and storage resources to process video streams at peak load at all times, without the rate of processing falling behind the rate of video input into the system. For example, the system is provisioned with sufficient CPU, memory, bus and disk, to execute the more computationally complex analyses of the video streams. Furthermore, if such systems are designed to concurrently process multiple streams of video, the system must be provisioned with enough computational and storage resources to process multiple video streams at peak load at all times. Such architectures use the system resources inefficiently because at times when the system is not at peak load, a significant portion of the resources are idle. In addition, systems that are configured for handling peak loading are likely to be quite costly.
In order to minimize the amount of resources in a system, one approach is to provision a system with enough resources to perform the complex analyses, but only on one video stream or, perhaps, concurrently on a limited number of video streams. Another approach is to provision a system to concurrently analyze many streams of video, but only provide the capability to perform the more rudimentary analyses.
Based on the foregoing, there is room for improvement in systems for analyzing the content of video streams. Specifically, there is a need for techniques for efficiently and concurrently performing complex analyses on multiple video streams.
The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.